Vortrag UNIX-Stammtisch 04/98

Convolution and, as a special case, autoconvolution of functions are important in many branches of mathematics and have found lots of applications, such as in physics, statistics, image processing and others. While it is a relatively easy task to determine the autoconvolution of a function (at least from the numerical point of view), the inverse problem, which consists in reconstructing a function from its autoconvolution is an ill-posed problem. Hence there is no possibility to solve such an inverse autoconvolution problem with a simple algebraic operation. Instead the problem has to be regularized, which means that it is replaced by a well-posed problem, which is close to the original problem in a certain sense. The outline of this thesis is as follows: In the first chapter we give an introduction to the type of inverse problems we consider, including some basic definitions and some important examples of regularization methods for these problems. At the end of the introduction we shortly present some general results about the convergence theory of Tikhonov-regularization. The second chapter is concerned with the autoconvolution of square integrable functions defined on the interval [0, 1]. This will lead us to the classical autoconvolution problems, where the term “classical” means that no kernel function is involved in the autoconvolution operator. For the data situation we distinguish two cases, namely data on [0, 1] and data on [0, 2]. We present some well-known properties of the classical autoconvolution operators. Moreover, we investigate nonlinearity conditions, which are required to show applicability of certain regularization approaches or which lead convergence rates for the Tikhonov regularization. For the inverse autoconvolution problem with data on the interval [0, 1] we show that a convergence rate cannot be shown using the standard convergence rate theory. If the data are given on the interval [0, 2], we can show a convergence rate for Tikhonov regularization if the exact solution satisfies a sparsity assumption. After these theoretical investigations we present various approaches to solve inverse autoconvolution problems. Here we focus on a discretized Lavrentiev regularization approach, for which even a convergence rate can be shown. Finally, we present numerical examples for the regularization methods we presented. In the third chapter we describe a physical measurement technique, the so-called SD-Spider, which leads to an inverse problem of autoconvolution type. The SD-Spider method is an approach to measure ultrashort laser pulses (laser pulses with time duration in the range of femtoseconds). Therefor we first present some very basic concepts of nonlinear optics and after that we describe the method in detail. Then we show how this approach, starting from the wave equation, leads to a kernel-based equation of autoconvolution type. The aim of chapter four is to investigate the equation and the corresponding problem, which we derived in chapter three. As a generalization of the classical autoconvolution we define the kernel-based autoconvolution operator and show that many properties of the classical autoconvolution operator can also be shown in this new situation. Moreover, we will consider inverse problems with kernel-based autoconvolution operator, which reflect the data situation of the physical problem. It turns out that these inverse problems may be locally well-posed, if all possible data are taken into account and they are locally ill-posed if one special part of the data is not available. Finally, we introduce reconstruction approaches for solving these inverse problems numerically and test them on real and artificial data.

Zugl.: Bielefeld, Univ., Diss., 2009

Zugl.: Chemnitz, Techn. Univ., Diss., 2005. / Ersch. im Verl. Kovač, (ISBN 3-8300-1884-3, 978-3-8300-1884-1), in der Schriftenr.: Chemnitzer Beiträge zur Sozialpädagogik, Bd. 2.

Zugl.: Bielefeld, Universiẗat, Diss., 2009.

Universiẗat, Diss., 2004--Bielefeld.

Please read the abstract in the section 00front of this document / Dissertation (MEd)--University of Pretoria, 2009. / Orthopaedic Surgery / unrestricted

Angesichts der zunehmenden Bedeutung ortsunabhängigen Arbeitens und der damit verbundenen Nachfrage nach flexiblen Testmethoden gewinnen Remote Usability-Tests (RUT) immer mehr an Bedeutung. Obwohl sie aufgrund ihres geringen Ressourcenbedarfs attraktiv sind, stellt sich die Frage, ob dies zulasten der Ergebnisqualität geht. Ziel dieser Arbeit ist es daher, den Aufwand und Nutzen von RUT zu untersuchen. Hierfür wurde zunächst der theoretische Hintergrund und aktuelle Forschungsstand von RUT dargestellt, einschließlich der historischen Entwicklung, verschiedener Durchführungsformen und Einflussfaktoren auf Aufwand und Nutzen. Im empirischen Teil wurde ein asynchroner, selbstmoderierter RUT am Beispiel eines Online-Shops durchgeführt. In der Datenanalyse wurde insbesondere der Zusammenhang zwischen der Probandenanzahl und der Anzahl und Art von Usability-Problemen untersucht. Abschließend wurden die Ergebnisse diskutiert und Empfehlungen hinsichtlich eines günstigen Verhältnisses von Aufwand und Nutzen bei RUT entwickelt.:Inhaltsverzeichnis 3 Abbildungsverzeichnis 7 Tabellenverzeichnis 9 Abkürzungsverzeichnis 10 I. Einleitung 11 1. Relevanz des Themas 11 2. Zielstellung 11 3. Methodisches Vorgehen und Aufbau 12 II. Theoretische Grundlagen 13 1. Begriffe und Definitionen 13 2. Usability-Evaluation 15 3. Historische Entwicklung des Remote Usability-Testings 18 4. Formen des Remote Usability-Testing 23 5. Aufwand und Nutzen in Remote Usability-Tests 40 6. Effektivität von Remote Usability-Tests 59 III. Korrelationsstudie 65 1. Konkretisierung der Problemstellung 65 2. Entwicklung von Hypothesen 66 3. Untersuchungsmethodik 68 4. Durchführung 86 5. Darstellung der Ergebnisse 90 6. Diskussion 101 6.1. Aufdeckung von Problemen 101 IV. Zusammenfassung und Fazit 118 Literaturverzeichnis 120 Anhang 126 Selbstständigkeitserklärung 141

The authors study parameter choice strategies for Tikhonov regularization of nonlinear ill-posed problems in Banach spaces. The effectiveness of any parameter choice for obtaining convergence rates depend on the interplay of the solution smoothness and the nonlinearity structure, and it can be expressed concisely in terms of variational inequalities. Such inequalities are link conditions between the penalty term, the norm misfit and the corresponding error measure. The parameter choices under consideration include an a priori choice, the discrepancy principle as well as the Lepskii principle. For the convenience of the reader the authors review in an appendix a few instances where the validity of a variational inequality can be established.

The authors study parameter choice strategies for Tikhonov regularization of nonlinear ill-posed problems in Banach spaces. The effectiveness of any parameter choice for obtaining convergence rates depend on the interplay of the solution smoothness and the nonlinearity structure, and it can be expressed concisely in terms of variational inequalities. Such inequalities are link conditions between the penalty term, the norm misfit and the corresponding error measure. The parameter choices under consideration include an a priori choice, the discrepancy principle as well as the Lepskii principle. For the convenience of the reader the authors review in an appendix a few instances where the validity of a variational inequality can be established.